Big Data challenges in smart Grid IoT (WAMS) deployment

Gore, Rahul; Valsan, Simi P.

doi:10.1109/comsnets.2016.7439953

Cited by 15 publications

(4 citation statements)

References 5 publications

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“…Substation automation is the act of automatically controlling the substation via instrumentation and control devices and refers to using data from intelligent electronic devices (IED), control and automation capabilities within the substation, and control commands from remote users using SCADA to control power system (switchyard) devices [31]. It is a technology applied to SG to control, protection, measurement, monitoring setting and monitoring protective relays, control and monitoring of auxiliary power systems voltage regulation [31][32]. IEDs are a set of power system equipment protective relaying devices, on load tap changer controllers, circuit breaker controllers, capacitor bank switches, recloser controllers, voltage regulators, etc.…”

Section: Sg Technologiesmentioning

confidence: 99%

Reliability Optimization of Smart Grid Based on Optimal Failure Rate Using Distributed Generation

Lekunze,

Kenfack,

Dandoussou

2024

Preprint

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This paper evaluates the optimal budget of distributed generation (DG) units that could be used to substitute preventive maintenance in an electric power distribution system. Preventive maintenance is a tradition performed on power distribution system to reduce its failure rate and increase its reliability. In order to perform this, a yearly budget is allocated. As time goes on, this budget becomes significant. With the aim to safe cost, this research has investigated on how DG units could be used as a backup system whenever there is a failure in the grid while waiting for the fault to be remediated. A method has been proposed on the time length above which the DG units as backup systems become more economical over the yearly preventive maintenance. Results have shown that when this duration is below the lifespan of the DG units used, it is feasible and DG units become the cheapest option.

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Section: Sg Technologiesmentioning

confidence: 99%

Reliability Optimization of Smart Grid Based on Optimal Failure Rate Using Distributed Generation

Lekunze,

Kenfack,

Dandoussou

2024

Preprint

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“…Specifically tailored for resourceconstrained devices, it presents several avenues for mitigating Big Data challenges: The complex challenges and problems associated with Big Data in the Internet of Things (IoT) paradigm are diverse and complex, encompassing numerous aspects such as data management, processing, unstructured data analytics, visualization, interoperability, data semantics, scalability, data fusion, integration, quality, and discovery [58]. These issues are closely related to the growing trend of "big data" within cloud computing environments and the progressive development of IoT technologies, exerting a significant impact on various industries, including but not limited to the power sector, smart cities, and large-scale petrochemical plants [58][59][60]. Additionally, the realm of IoT architectures is not immune to pressing security and privacy threats, making them salient challenges that require immediate and effective addressal [61].…”

Section: Tinyml As a Novel Facilitator In Iot Big Data Managementmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

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“…To intelligently operate EPSs and effectively balance the demand and supply, it is crucial to collect real-time data from both transmission and distribution networks. To this aim, IIoT systems can be implemented, and the required data can be collected using smart meters, intelligent feeders, Phasor Measurement Units (PMUs), and micro PMUs [31]. This data can be processed for forecasting the load, estimating the states of the system, and controlling the EPS in a distributed manner.…”

Section: ) Digitizing Power Generationmentioning

confidence: 99%

Cyber-Security of Industrial Internet of Things in Electric Power Systems

2022

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Electric Power Systems (EPSs) are among the most critical infrastructures of any society, since they significantly impact other infrastructures. Recently, there has been a trend toward implementing modern technologies, such as Industrial Internet of Things (IIoT), in EPSs to enhance their real-time monitoring, control, situational awareness, and intelligence. This movement, however, has exposed EPSs to various cyber intrusions that originate from the IIoT ecosystem. Statistics show that 38% of reported attacks have been against power and water infrastructure, and so far at least 91% of power utilities have experienced a cyber-attack. The cyber-security problem is even more severe for IIoT applications in EPSs due to the vulnerabilities and resource limitations of such applications. Thus, based on the above statistics, it is necessary to investigate the vulnerabilities of IIoT-based applications in EPSs, identify probable attacks and their consequences, and develop intrusion prevention and detection approaches to secure IIoT systems. On this basis, this paper first elaborates on the applications of IIoT-based systems in EPSs, and evaluates their security challenges. Afterwards, it comprehensively reviews various cyber-attacks against IIoT-assisted EPSs, with a particular focus on attack entry points and adversarial methods. Finally, efforts to prevent cyber-intrusions against IIoT systems in EPSs are explained, and different attack detection techniques are discussed.INDEX TERMS Cyber-attacks, cyber-security, electric power systems, industrial internet of things, intrusion detection systems. I. TRANSFORMATION OF IOT TO IIOT

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Big Data challenges in smart Grid IoT (WAMS) deployment

Cited by 15 publications

References 5 publications

Reliability Optimization of Smart Grid Based on Optimal Failure Rate Using Distributed Generation

Reliability Optimization of Smart Grid Based on Optimal Failure Rate Using Distributed Generation

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Cyber-Security of Industrial Internet of Things in Electric Power Systems

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